
import os

## Set directory
os.chdir('/hpc/group/pbenfeylab/CheWei/CW_data/genesys')

import networkx as nx
from genesys_evaluate_v1 import *
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import warnings
# Suppress all warning messages
warnings.filterwarnings("ignore", category=DeprecationWarning)


## Conda Env pytorch-gpu on DCC
print(torch.__version__)
print(sc.__version__)

1.13.0.post200
1.9.1


## Genes considered/used (shared among samples) 
gene_list = pd.read_csv('./gene_list_1108.csv')


with open("./genesys_root_data.pkl", 'rb') as file_handle:
    data = pickle.load(file_handle)
    
batch_size = 2000
dataset = Root_Dataset(data['X_test'], data['y_test'])
loader = DataLoader(dataset,
                         batch_size = batch_size,
                         shuffle = True, drop_last=True)


input_size = data['X_train'].shape[1]
## 10 cell types 
output_size = 10
embedding_dim = 256
hidden_dim = 256
n_layers = 2
device = "cpu"
path = "./"


model = ClassifierLSTM(input_size, output_size, embedding_dim, hidden_dim, n_layers).to(device)
model.load_state_dict(torch.load(path+"best_ALL_1130_continue.pth", map_location=torch.device('cpu')))
model = model
model.eval()

ClassifierLSTM(
  (fc1): Sequential(
    (0): Linear(in_features=17513, out_features=256, bias=True)
    (1): Dropout(p=0.2, inplace=False)
    (2): GaussianNoise()
  )
  (fc): Sequential(
    (0): ReLU()
    (1): Linear(in_features=512, out_features=512, bias=True)
    (2): ReLU()
    (3): Linear(in_features=512, out_features=10, bias=True)
  )
  (lstm): LSTM(256, 256, num_layers=2, batch_first=True, dropout=0.2, bidirectional=True)
  (dropout): Dropout(p=0.2, inplace=False)
  (b_to_z): DBlock(
    (fc1): Linear(in_features=512, out_features=256, bias=True)
    (fc2): Linear(in_features=512, out_features=256, bias=True)
    (fc_mu): Linear(in_features=256, out_features=512, bias=True)
    (fc_logsigma): Linear(in_features=256, out_features=512, bias=True)
  )
  (bz2_infer_z1): DBlock(
    (fc1): Linear(in_features=1024, out_features=256, bias=True)
    (fc2): Linear(in_features=1024, out_features=256, bias=True)
    (fc_mu): Linear(in_features=256, out_features=512, bias=True)
    (fc_logsigma): Linear(in_features=256, out_features=512, bias=True)
  )
  (z1_to_z2): DBlock(
    (fc1): Linear(in_features=512, out_features=256, bias=True)
    (fc2): Linear(in_features=512, out_features=256, bias=True)
    (fc_mu): Linear(in_features=256, out_features=512, bias=True)
    (fc_logsigma): Linear(in_features=256, out_features=512, bias=True)
  )
  (z_to_x): Decoder(
    (fc1): Linear(in_features=512, out_features=256, bias=True)
    (fc2): Linear(in_features=256, out_features=256, bias=True)
    (fc3): Linear(in_features=256, out_features=17513, bias=True)
  )
)


classes = ['Columella', 'Lateral Root Cap', 'Phloem', 'Xylem', 'Procambium', 'Pericycle', 'Endodermis', 'Cortex', 'Atrichoblast', 'Trichoblast']
class2num = {c: i for (i, c) in enumerate(classes)}
num2class = {i: c for (i, c) in enumerate(classes)}


cts = ['Atrichoblast','Trichoblast','Cortex','Endodermis','Pericycle','Procambium','Xylem','Phloem','Lateral Root Cap','Columella']
ctw = np.zeros((len(cts), 17513, 17513))
## number of cells sampled from the atlas
batch_size = 2000


## GRN for the transition t3 to t5
for ct in cts:
    print(ct)
    cws = np.zeros((len(loader), 17513, 17513))
    with torch.no_grad():
        for i, sample in enumerate(loader):
            x = sample['x'].to(device)
            y = sample['y'].to(device)
            y_label = [num2class[i] for i in y.tolist()]
            
            pred_h = model.init_hidden(batch_size)
            tfrom = model.generate_next(x, pred_h, 2).to('cpu').detach().numpy()
            cfrom = tfrom[np.where(np.array(y_label)==ct)[0],:]
            
            pred_h = model.init_hidden(batch_size)
            tto = model.generate_next(x, pred_h, 4).to('cpu').detach().numpy()   
            cto = tto[np.where(np.array(y_label)==ct)[0],:]
            
            cw = torch.linalg.lstsq(torch.tensor(cfrom), torch.tensor(cto)).solution.detach().numpy()
            cws[i] = cw
    
    ## Calculate mean across number of repeats
    cwm = np.mean(cws, axis=0)
    ctw[cts.index(ct)] = cwm

Atrichoblast
Trichoblast


# Save the array to disk
np.save('genesys_ctw_t3-t5.npy', ctw)


ctw = np.load('genesys_ctw_t3-t5.npy')


## Calculate z-scores
ctw_z = np.zeros((len(cts), 17513, 17513))
for i in range(len(cts)):
    ctw_z[i] = (ctw[i] - np.mean(ctw[i])) / np.std(ctw[i])


## Filtering based on z-scores (with no weights)
ctw_f = np.zeros((len(cts), 17513, 17513))
## z-score threshold (keep values > mean + threshold*std)
threshold=3
for i in range(len(cts)):
    ctw_f[i] = np.abs(ctw_z[i]) > threshold


wanted_TFs = pd.read_csv("./Kay_TF_thalemine_annotations.csv")


## Make TF names unique and assign preferred names
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G33880"]="WOX9"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G45160"]="SCL27"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G04410"]="NAC78"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G29035"]="ORS1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G02540"]="ZHD3"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G16500"]="IAA26"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G09740"]="HAG5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT4G24660"]="ZHD2"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G46880"]="HDG5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G28420"]="RLT1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G14580"]="BLJ"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G45260"]="BIB"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G02070"]="RVN"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G28160"]="FIT"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G68360"]="GIS3"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G20640"]="NLP4"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G05550"]="VFP5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G59470"]="FRF1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G15150"]="HAT7"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G14750"]="WER"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G75710"]="BRON"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G74500"]="TMO7"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G12646"]="RITF1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G48100"]="ARR5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT4G16141"]="GATA17L"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G65640"]="NFL"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G62700"]="VND5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT4G36160"]="VND2"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G66300"]="VND3"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G12260"]="VND4"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G62380"]="VND6"


pd.Series(wanted_TFs['Name']).value_counts().head(5)

NAC001    1
PRE5      1
MYB118    1
MYB21     1
MYB0      1
Name: Name, dtype: int64


TFidx = []
for i in wanted_TFs['GeneID']:
    if i in gene_list['features'].tolist():
        TFidx.append(np.where(gene_list['features']==i)[0][0])

TFidx = np.sort(np.array(TFidx))


def network(i):
    ## No weights
    adj_nw = ctw_f[i]
    ## Weighted
    adj = ctw[i]*ctw_f[i]
    ## TF only
    adj = adj[np.ix_(TFidx,TFidx)]
    adj_nw = adj_nw[np.ix_(TFidx,TFidx)]
    
    ## Remove no connect 
    regidx = np.sort(np.array(pd.Series(np.where(adj_nw==True)[0]).value_counts().index[pd.Series(np.where(adj_nw==True)[0]).value_counts()>=1]))
    taridx = np.sort(np.array(pd.Series(np.where(adj_nw==True)[1]).value_counts().index[pd.Series(np.where(adj_nw==True)[1]).value_counts()>=1]))
    ## Reciprocol
    keepidx = np.sort(np.array(list(set(regidx).intersection(taridx))))
    #keepidx = np.sort(np.array(list(set(regidx).union(taridx))))
    
    TFID = np.array(gene_list['features'][TFidx])[keepidx].tolist()
    ## TF name to keep
    TFname = []
    for i in np.array(gene_list['features'][TFidx])[keepidx]:
        TFname.append(wanted_TFs['Name'][np.where(wanted_TFs['GeneID']==i)[0][0]])
        
    adj = adj[np.ix_(keepidx,keepidx)]
    
    # Create a NetworkX graph for non-directed edges
    G = nx.Graph()  # supports directed edges and allows for multiple edges between the same pair of nodes
    
    # Add nodes to the graph
    num_nodes = adj.shape[0]
    for i, name in enumerate(TFname):
        G.add_node(i, name=name)
    
    # Add edges to the graph with weights
    for i in range(num_nodes):
        for j in range(num_nodes):
            weight = adj[i, j]
            if weight != 0:
                G.add_edge(i, j, weight=abs(weight), distance=1/abs(weight))
            

    ## Measures the extent to which how close a node is to all other nodes in the network, considering the shortest paths or geodesic distances between nodes
    closeness_centrality = nx.closeness_centrality(G, distance='distance')
    ## Measures the extent to which a node that are not only well-connected but also connected to other well-connected nodes.
    eigenvector_centrality = nx.eigenvector_centrality(G)
    
    # Create a NetworkX graph for diected edges
    G = nx.MultiDiGraph()  # supports directed edges and allows for multiple edges between the same pair of nodes
    
    # Add nodes to the graph
    num_nodes = adj.shape[0]
    for i, name in enumerate(TFname):
        G.add_node(i, name=name)
    
    # Add edges to the graph with weights
    for i in range(num_nodes):
        for j in range(num_nodes):
            weight = adj[i, j]
            if weight != 0:
                G.add_edge(i, j, weight=weight)
    
    ## Measures the number of connections (edges) each node has
    degree_centrality = nx.degree_centrality(G)
    # Calculate outgoing centrality
    out_centrality = nx.out_degree_centrality(G)
    # Calculate incoming centrality
    in_centrality = nx.in_degree_centrality(G)
    ## Measures the extent to which a node lies on the shortest paths between other nodes.
    betweenness_centrality = nx.betweenness_centrality(G, weight='weight')
    
    ## Non_Reciprocal Out centrality
    # Visualize the graph
    pos = nx.spring_layout(G)  # Positions of the nodes
    
    # Node colors based on weighted betweenness centrality
    node_colors = [out_centrality[node] for node in G.nodes()]
    
    # Node sizes based on weighted betweenness centrality
    node_sizes = [out_centrality[node] * 1000 for node in G.nodes()]

    # Get the edge weights as a dictionary
    edge_weights = nx.get_edge_attributes(G, 'weight')
    edge_colors = ['red' if weight > 0 else 'blue' for (_, _, weight) in G.edges(data='weight')]
    
    # Scale the edge weights to desired linewidths
    max_weight = max(edge_weights.values())
    edge_widths = [float(edge_weights[edge]) / max_weight for edge in G.edges]
    
    # Draw the graph
    nx.draw(G, pos=pos, node_color=node_colors, node_size=node_sizes, with_labels=False, width=edge_widths, edge_color=edge_colors)
    # Add node labels
    labels = {node: G.nodes[node]['name'] for node in G.nodes}
    nx.draw_networkx_labels(G, pos=pos, labels=labels, font_size=8)
    
    # Add a colorbar to show the weighted betweenness centrality color mapping
    sm = plt.cm.ScalarMappable(cmap='viridis', norm=plt.Normalize(vmin=min(node_colors), vmax=max(node_colors)))
    sm.set_array([])
    plt.colorbar(sm)
    
    # Show the plot
    plt.show()
    
    dc = pd.DataFrame.from_dict(degree_centrality, orient='index', columns=['degree_centrality'])
    oc = pd.DataFrame.from_dict(out_centrality, orient='index', columns=['out_centrality'])
    ic = pd.DataFrame.from_dict(in_centrality, orient='index', columns=['in_centrality'])
    bc = pd.DataFrame.from_dict(betweenness_centrality, orient='index', columns=['betweenness_centrality'])
    cc = pd.DataFrame.from_dict(closeness_centrality, orient='index', columns=['closeness_centrality'])
    ec = pd.DataFrame.from_dict(eigenvector_centrality, orient='index', columns=['eigenvector_centrality'])
    df = pd.concat([dc,oc,ic,bc,cc,ec], axis=1)
    df.index =TFname
    df = df.sort_values('betweenness_centrality', ascending=False)
    
    return(df)


atri = network(0)


tri = network(1)


cor = network(2)


end = network(3)


per = network(4)


pro = network(5)


xyl = network(6)


phl = network(7)


lrc = network(8)


col = network(9)


atri.columns = ['atri_degree_centrality','atri_out_centrality','atri_in_centrality','atri_betweenness_centrality','atri_closeness_centrality','atri_eigenvector_centrality']
tri.columns = ['tri_degree_centrality','tri_out_centrality','tri_in_centrality','tri_betweenness_centrality','tri_closeness_centrality','tri_eigenvector_centrality']
cor.columns = ['cor_degree_centrality','cor_out_centrality','cor_in_centrality','cor_betweenness_centrality','cor_closeness_centrality','cor_eigenvector_centrality']
end.columns = ['end_degree_centrality','end_out_centrality','end_in_centrality','end_betweenness_centrality','end_closeness_centrality','end_eigenvector_centrality']
per.columns = ['per_degree_centrality','per_out_centrality','per_in_centrality','per_betweenness_centrality','per_closeness_centrality','per_eigenvector_centrality']
pro.columns = ['pro_degree_centrality','pro_out_centrality','pro_in_centrality','pro_betweenness_centrality','pro_closeness_centrality','pro_eigenvector_centrality']
xyl.columns = ['xyl_degree_centrality','xyl_out_centrality','xyl_in_centrality','xyl_betweenness_centrality','xyl_closeness_centrality','xyl_eigenvector_centrality']
phl.columns = ['phl_degree_centrality','phl_out_centrality','phl_in_centrality','phl_betweenness_centrality','phl_closeness_centrality','phl_eigenvector_centrality']
lrc.columns = ['lrc_degree_centrality','lrc_out_centrality','lrc_in_centrality','lrc_betweenness_centrality','lrc_closeness_centrality','lrc_eigenvector_centrality']
col.columns = ['col_degree_centrality','col_out_centrality','col_in_centrality','col_betweenness_centrality','col_closeness_centrality','col_eigenvector_centrality']


## Indentify main regulators in each net work
tff = []
tff = tff + atri[atri['atri_betweenness_centrality']>0].index.tolist()
tff = tff + tri[tri['tri_betweenness_centrality']>0].index.tolist()
tff = tff + lrc[lrc['lrc_betweenness_centrality']>0].index.tolist()
tff = tff + cor[cor['cor_betweenness_centrality']>0].index.tolist()
tff = tff + end[end['end_betweenness_centrality']>0].index.tolist()
tff = tff + per[per['per_betweenness_centrality']>0].index.tolist()
tff = tff + pro[pro['pro_betweenness_centrality']>0].index.tolist()
tff = tff + xyl[xyl['xyl_betweenness_centrality']>0].index.tolist()
tff = tff + phl[phl['phl_betweenness_centrality']>0].index.tolist()
tff = tff + col[col['col_betweenness_centrality']>0].index.tolist()
tf_occurance = pd.DataFrame(pd.Series(tff).value_counts(), columns=['tf_occurance'])
tf_spec = pd.concat([tf_occurance, atri, tri, lrc, cor, end, per, pro, xyl, phl, col], axis=1)
tf_spec = tf_spec.fillna(0)


## Epidermis (atri, tri, lrc)
celltype1='atri'
celltype2='tri'
celltype3='lrc'
ts = tf_spec[tf_spec['tf_occurance']==3][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype3+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype3+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality', celltype3+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==9].sort_values(['centrality_count','centrality_sum'], ascending=False)


## atri, tri
celltype1='atri'
celltype2='tri'
ts = tf_spec[tf_spec['tf_occurance']==2][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==6].sort_values(['centrality_count','centrality_sum'], ascending=False)


## Atrichoblast specific
celltype = 'atri'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)


## Trichoblast specific
celltype = 'tri'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)


## LRC specific
celltype = 'lrc'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)


## Columella specific
celltype = 'col'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)


## Ground tissue
celltype1='cor'
celltype2='end'
ts = tf_spec[tf_spec['tf_occurance']==2][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==6].sort_values(['centrality_count','centrality_sum'], ascending=False)


## Cortex specific
celltype = 'cor'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)


## Endodermis specific
celltype = 'end'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)


## Stele
celltype1='per'
celltype2='pro'
celltype3='xyl'
celltype4='phl'
ts = tf_spec[tf_spec['tf_occurance']==4][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype3+'_betweenness_centrality', celltype4+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype3+'_out_centrality', celltype4+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality', celltype3+'_in_centrality', celltype4+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==12].sort_values(['centrality_count','centrality_sum'], ascending=False)


## Pericycle
celltype = 'per'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)


## Procambium
celltype = 'pro'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)


## Xylem
celltype = 'xyl'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)


## Phloem
celltype = 'phl'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)


gene = 'SHR'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]


gene = 'BLJ'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]


gene = 'JKD'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]


gene = 'RVN'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]


gene = 'BIB'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]


gene = 'IME'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]


gene = 'MYB66'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]


gene = 'GL2'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]


tf_spec.to_csv('TF_GRN_centrality_t3-t5_zscore3.csv', index=True)

	atri_betweenness_centrality	tri_betweenness_centrality	lrc_betweenness_centrality	atri_out_centrality	tri_out_centrality	lrc_out_centrality	atri_in_centrality	tri_in_centrality	lrc_in_centrality	centrality_count	centrality_sum
GL2	0.953274	0.001667	0.628124	0.927046	0.162257	0.039531	0.350534	0.003527	0.127379	9	12.193340
ATS	0.815165	0.005550	0.750948	0.183274	0.153439	0.163982	0.170819	0.049383	0.188873	9	11.481432
TTG2	0.927633	0.000745	0.007653	0.425267	0.146384	0.062958	0.720641	0.005291	0.105417	9	11.401989
IAA14	0.449128	0.199971	0.017018	0.265125	0.192240	0.098097	0.174377	0.068783	0.049780	9	10.514518
FIT	0.585533	0.006556	0.002656	0.024911	0.019400	0.017570	0.163701	0.019400	0.117130	9	9.956857

	atri_betweenness_centrality	tri_betweenness_centrality	atri_out_centrality	tri_out_centrality	atri_in_centrality	tri_in_centrality	centrality_count	centrality_sum
AT3G53370	0.238250	0.717427	0.087189	0.276896	0.051601	0.871252	6	8.242615
AT2G37120	0.004485	0.551287	0.099644	0.116402	0.115658	0.252205	6	7.139681

	atri_betweenness_centrality	atri_out_centrality	atri_in_centrality	centrality_count	centrality_sum
KAN	0.487970	0.023132	0.049822	3	3.560923
CRF4	0.399243	0.110320	0.069395	3	3.578958
OFP18	0.343676	0.088968	0.131673	3	3.564317
TRY	0.322683	0.560498	0.062278	3	3.945458
ARR6	0.007086	0.003559	0.040925	3	3.051570
GRF3	0.002303	0.037367	0.024911	3	3.064580
AT4G31650	0.001773	0.035587	0.067616	3	3.104976
AT3G52100	0.001725	0.010676	0.012456	3	3.024857
GIS3	0.001021	0.133452	0.007117	3	3.141591

	tri_betweenness_centrality	tri_out_centrality	tri_in_centrality	centrality_count	centrality_sum
RSL4	0.969429	0.058201	0.604938	3	4.632568
LRL3	0.951605	0.204586	0.784832	3	4.941023
RSL2	0.795929	0.068783	0.421517	3	4.286228
AT4G39160	0.611684	0.067019	0.194004	3	3.872707
AT4G09100	0.611622	0.035273	0.477954	3	4.124850
ATMYC1	0.485395	0.373898	0.112875	3	3.972168
RL6	0.477487	0.100529	0.054674	3	3.632690
AT5G56200	0.422863	0.003527	0.052910	3	3.479300
RHD6	0.249328	0.128748	0.343915	3	3.721992
RAP2.11	0.220082	0.024691	0.098765	3	3.343538
AT5G44260	0.212372	0.097002	0.072310	3	3.381685
RSL1	0.054203	0.134039	0.042328	3	3.230570
LSD1	0.016484	0.021164	0.040564	3	3.078212
ARR5	0.008229	0.007055	0.049383	3	3.064667
WRKY61	0.005291	0.010582	0.102293	3	3.118166
EGL3	0.003069	0.123457	0.022928	3	3.149454
AT5G04390	0.001736	0.012346	0.065256	3	3.079337
AT5G11340	0.000932	0.021164	0.029982	3	3.052078
MYB45	0.000059	0.008818	0.042328	3	3.051206
MBD4	0.000003	0.049383	0.007055	3	3.056441

	lrc_betweenness_centrality	lrc_out_centrality	lrc_in_centrality	centrality_count	centrality_sum
BRON	0.825382	0.199122	0.541728	3	4.566231
ERF9	0.767259	0.363104	0.260615	3	4.390978
CHR38	0.747223	0.185944	0.190337	3	4.123504
ASL1	0.735740	0.049780	0.260615	3	4.046135
AT3G60670	0.721004	0.207906	0.142020	3	4.070931
RITF1	0.710495	0.193265	0.165447	3	4.069207
LBD4	0.708130	0.188873	0.158126	3	4.055128
AT2G36930	0.703613	0.033675	0.109810	3	3.847097
AT2G46160	0.696178	0.095168	0.079063	3	3.870410
IAA33	0.692765	0.450952	0.142020	3	4.285737
FEZ	0.663225	0.607613	0.118594	3	4.389433
GRF2	0.547155	0.098097	0.062958	3	3.708209
GATA17L	0.158240	0.077599	0.076135	3	3.311973
SR1	0.086736	0.057101	0.046852	3	3.190689
WRI4	0.080639	0.273792	0.016105	3	3.370536
NAC052	0.020292	0.051245	0.040996	3	3.112532
WRI1	0.011842	0.054173	0.058565	3	3.124580
AT2G36026	0.008280	0.032211	0.052709	3	3.093200
NTT	0.004880	0.054173	0.046852	3	3.105905
NAC016	0.003287	0.102489	0.090776	3	3.196552
AGL20	0.002995	0.079063	0.058565	3	3.140623
HMGB3	0.001713	0.033675	0.043924	3	3.079312
GATA7	0.001368	0.102489	0.042460	3	3.146316
AT1G27050	0.000721	0.039531	0.011713	3	3.051966
MAF5	0.000067	0.020498	0.061493	3	3.082058
WRKY35	0.000064	0.017570	0.098097	3	3.115731
AGL94	0.000002	0.055637	0.089312	3	3.144951

Load Data¶

Load trained GeneSys model¶

Load TFs list¶

Network analysis¶

Search for individual genes¶

	col_betweenness_centrality	col_out_centrality	col_in_centrality	centrality_count	centrality_sum
LBD41	0.850319	0.016082	0.507310	3	4.373710
NAM	0.709777	0.024854	0.220760	3	3.955391
AT5G41020	0.449368	0.051170	0.043860	3	3.544397
WRKY14	0.277934	0.045322	0.209064	3	3.532320
TRP1	0.264228	0.008772	0.092105	3	3.365105
PIF3	0.192704	0.228070	0.021930	3	3.442704
HSL1	0.129969	0.023392	0.086257	3	3.239618
AT3G12977	0.087989	0.024854	0.228070	3	3.340913
BZIP25	0.020354	0.049708	0.171053	3	3.241115
AT2G42300	0.009894	0.042398	0.160819	3	3.213110
SCL8	0.006447	0.198830	0.209064	3	3.414342
AT5G06110	0.004373	0.019006	0.064327	3	3.087706
RAP2.1	0.003129	0.024854	0.092105	3	3.120089
NAC082	0.001460	0.017544	0.021930	3	3.040934
JMJ18	0.001460	0.008772	0.046784	3	3.057015
GBF4	0.001460	0.010234	0.026316	3	3.038010
MBD10	0.001456	0.032164	0.077485	3	3.111105
AT5G14140	0.000997	0.039474	0.021930	3	3.062401
SPL14	0.000477	0.135965	0.062865	3	3.199308
AT2G44430	0.000432	0.068713	0.054094	3	3.123239
AT1G74250	0.000415	0.040936	0.051170	3	3.092521
STOP1	0.000060	0.095029	0.143275	3	3.238364
AT5G12400	0.000011	0.114035	0.039474	3	3.153519
BZIP28	0.000004	0.083333	0.017544	3	3.100881

	cor_betweenness_centrality	end_betweenness_centrality	cor_out_centrality	end_out_centrality	cor_in_centrality	end_in_centrality	centrality_count	centrality_sum
MYB74	0.067311	0.265845	0.542308	0.749035	0.007692	0.996139	6	8.628331
JKD	0.032763	0.053518	0.300000	0.284427	0.532692	0.471042	6	7.674443
MYB68	0.000022	0.008478	0.275000	0.374517	0.001923	0.984556	6	7.644497
RAX2	0.000174	0.010255	0.242308	0.158301	0.025000	0.970399	6	7.406437
BLJ	0.000122	0.006241	0.173077	0.105534	0.017308	0.962677	6	7.264959
...	...	...	...	...	...	...	...	...
ZFN3	0.000030	0.000073	0.011538	0.029601	0.017308	0.009009	6	6.067559
AT1G72010	0.000004	0.000041	0.013462	0.033462	0.003846	0.016731	6	6.067546
MYB13	0.000007	0.000053	0.007692	0.033462	0.019231	0.006435	6	6.066881
MBD7	0.000445	0.000456	0.007692	0.024453	0.011538	0.016731	6	6.061316
AT1G03350	0.000004	0.000159	0.007692	0.034749	0.005769	0.009009	6	6.057383

	cor_betweenness_centrality	cor_out_centrality	cor_in_centrality	centrality_count	centrality_sum
AT2G38300	0.006625	0.061538	0.532692	3	3.600856
MYB86	0.002905	0.048077	0.225000	3	3.275982
AT3G49930	0.001264	0.001923	0.032692	3	3.035879
AT2G42660	0.000815	0.065385	0.215385	3	3.281584
SHL1	0.000734	0.036538	0.007692	3	3.044964
IAA16	0.000463	0.040385	0.013462	3	3.054309
AT5G07810	0.000274	0.003846	0.032692	3	3.036813
RGL3	0.000233	0.026923	0.300000	3	3.327157
AT1G76350	0.000185	0.013462	0.007692	3	3.021339
AT3G04930	0.000089	0.007692	0.005769	3	3.013550
EMB1135	0.000089	0.005769	0.003846	3	3.009704
NAC003	0.000067	0.048077	0.050000	3	3.098144
AP2	0.000052	0.034615	0.028846	3	3.063513
AT5G14370	0.000044	0.001923	0.003846	3	3.005814
GLK2	0.000041	0.046154	0.042308	3	3.088502
LZF1	0.000026	0.019231	0.044231	3	3.063487
NFXL2	0.000019	0.001923	0.005769	3	3.007711
IDD7	0.000015	0.050000	0.003846	3	3.053861
ETR1	0.000011	0.019231	0.025000	3	3.044242
BRM	0.000011	0.013462	0.011538	3	3.025011
NPH4	0.000011	0.015385	0.017308	3	3.032703
maMYB	0.000011	0.015385	0.028846	3	3.044242
AT1G32810	0.000007	0.009615	0.001923	3	3.011546
RR12	0.000007	0.005769	0.017308	3	3.023084
PHL1	0.000004	0.007692	0.001923	3	3.009619
LUH	0.000004	0.005769	0.005769	3	3.011542
AT3G24490	0.000004	0.003846	0.001923	3	3.005773
RLT2	0.000004	0.021154	0.007692	3	3.028850
NTM1	0.000004	0.015385	0.001923	3	3.017311
NF-YB10	0.000004	0.011538	0.001923	3	3.013465
AT2G01060	0.000004	0.005769	0.001923	3	3.007696

	end_betweenness_centrality	end_out_centrality	end_in_centrality	centrality_count	centrality_sum
AT5G51790	0.038008	0.007722	0.059202	3	3.104932
AT2G45120	0.015278	0.007722	0.056628	3	3.079628
BIB	0.014205	0.064350	0.253539	3	3.332094
AT2G27930	0.009614	0.001287	0.153153	3	3.164055
4-Sep	0.006045	0.001287	0.006435	3	3.013767
...	...	...	...	...	...
AT2G03470	0.000003	0.045045	0.016731	3	3.061779
PHR1	0.000002	0.036036	0.005148	3	3.041186
BPC4	0.000002	0.041184	0.010296	3	3.051482
CCA1	0.000002	0.041184	0.020592	3	3.061778
AT1G19000	0.000002	0.016731	0.003861	3	3.020594

	per_betweenness_centrality	pro_betweenness_centrality	xyl_betweenness_centrality	phl_betweenness_centrality	per_out_centrality	pro_out_centrality	xyl_out_centrality	phl_out_centrality	per_in_centrality	pro_in_centrality	xyl_in_centrality	phl_in_centrality	centrality_count	centrality_sum
MYB88	0.009783	0.011002	0.203635	0.004870	0.115055	0.199792	0.488525	0.099626	0.122399	0.291363	0.337705	0.090909	12	13.974663
ATAUX2-11	0.000015	0.000028	0.704487	0.000036	0.088127	0.045786	0.013115	0.042341	0.037944	0.444329	0.560656	0.013699	12	13.950561
AT1G12440	0.000066	0.036612	0.000075	0.080959	0.166463	0.354839	0.373770	0.049813	0.095471	0.160250	0.111475	0.039851	12	13.469645
IAA11	0.005155	0.000192	0.000539	0.310565	0.057528	0.031217	0.232787	0.098381	0.187271	0.101977	0.259016	0.180573	12	13.465202

	per_betweenness_centrality	per_out_centrality	per_in_centrality	centrality_count	centrality_sum
AT2G20100	0.738585	0.025704	0.104039	3	3.868328
IDD11	0.447962	0.350061	0.075887	3	3.873911
GATA23	0.149711	0.189718	0.044064	3	3.383493
AT2G35605	0.040975	0.009792	0.009792	3	3.060559
AT1G27660	0.037554	0.077111	0.089351	3	3.204016
ERF12	0.025974	0.078335	0.265606	3	3.369915
AT3G21330	0.019212	0.033048	0.003672	3	3.055932
HDA3	0.004984	0.020808	0.057528	3	3.083320
AT2G44020	0.004485	0.009792	0.003672	3	3.017949
ATWHY2	0.003670	0.036720	0.059976	3	3.100366
WRKY4	0.002916	0.033048	0.056304	3	3.092267
IDD14	0.001750	0.195838	0.033048	3	3.230637
EIL3	0.001636	0.029376	0.023256	3	3.054268
AT2G40200	0.001533	0.037944	0.035496	3	3.074972
NAC080	0.001255	0.006120	0.040392	3	3.047767
JMJD5	0.001218	0.019584	0.007344	3	3.028146
AT2G35430	0.001215	0.024480	0.044064	3	3.069758
RVE1	0.001212	0.053856	0.068543	3	3.123611
AT4G34290	0.001201	0.022032	0.002448	3	3.025681
DOF1	0.000399	0.029376	0.053856	3	3.083630
STH	0.000252	0.018360	0.019584	3	3.038196
MYB60	0.000211	0.022032	0.024480	3	3.046723
PRT1	0.000111	0.084455	0.100367	3	3.184934
MYB51	0.000087	0.020808	0.061200	3	3.082094
SAC51	0.000015	0.037944	0.091799	3	3.129758

	pro_betweenness_centrality	pro_out_centrality	pro_in_centrality	centrality_count	centrality_sum
ASL9	0.293906	0.040583	0.035380	3	3.369869
HB21	0.048301	0.040583	0.118626	3	3.207511
AT2G36340	0.027752	0.107180	0.061394	3	3.196327
AT4G25410	0.014994	0.170656	0.110302	3	3.295951
AT4G17780	0.002061	0.001041	0.014568	3	3.017669
GAI	0.001636	0.058273	0.097815	3	3.157723
MYB10	0.001381	0.104058	0.037461	3	3.142900
HAT3	0.001025	0.004162	0.054110	3	3.059298
ARF19	0.000079	0.081165	0.046826	3	3.128071
HMGB6	0.000046	0.003122	0.006243	3	3.009411
IDD16	0.000002	0.001041	0.029136	3	3.030179

	xyl_betweenness_centrality	xyl_out_centrality	xyl_in_centrality	centrality_count	centrality_sum
VND2	0.896462	0.147541	0.970492	3	5.014495
IAA31	0.705878	0.186885	0.596721	3	4.489484
LBD31	0.699418	0.173770	0.078689	3	3.951877
VND7	0.673069	0.026230	0.816393	3	4.515692
AT1G66810	0.671506	0.098361	0.373770	3	4.143637
VND3	0.595308	0.062295	0.885246	3	4.542849
JLO	0.526478	0.209836	0.209836	3	3.946150
AT1G29950	0.512360	0.239344	0.006557	3	3.758261
IAA6	0.497390	0.085246	0.829508	3	4.412144
XND1	0.397972	0.081967	0.809836	3	4.289776
AT1G68810	0.385418	0.806557	0.970492	3	5.162468
ZHD3	0.339107	0.078689	0.262295	3	3.680091
VND1	0.330101	0.314754	0.537705	3	4.182560
MYB83	0.102955	0.081967	0.767213	3	3.952135
MYB99	0.048846	0.039344	0.193443	3	3.281633
AP3	0.046042	0.134426	0.472131	3	3.652599
IAA8	0.039991	0.557377	0.822951	3	4.420319
LBD18	0.039711	0.213115	0.291803	3	3.544629
TCP10	0.031460	0.200000	0.019672	3	3.251132
NAC010	0.030004	0.229508	0.137705	3	3.397217
HB31	0.013557	0.088525	0.173770	3	3.275852
VND5	0.012274	0.045902	0.731148	3	3.789323
AT1G26610	0.006460	0.009836	0.081967	3	3.098264
MYB46	0.003279	0.403279	0.901639	3	4.308197
BZIP49	0.003149	0.059016	0.072131	3	3.134297
AT1G68200	0.002610	0.259016	0.570492	3	3.832118
VND4	0.001607	0.052459	0.813115	3	3.867181
MYB85	0.000324	0.114754	0.029508	3	3.144586
SHP1	0.000075	0.177049	0.236066	3	3.413190

	phl_betweenness_centrality	phl_out_centrality	phl_in_centrality	centrality_count	centrality_sum
GATA19	0.554558	0.044832	0.014944	3	3.614334
AT2G03500	0.426435	0.625156	0.478207	3	4.529798
FLP	0.353997	0.087173	0.105853	3	3.547023
REM22	0.347550	0.110834	0.069738	3	3.528122
AT3G12730	0.265207	0.612702	0.745953	3	4.623862
AT1G47570	0.254227	0.080946	0.068493	3	3.403667
HCA2	0.116451	0.498132	0.323786	3	3.938369
NAC086	0.078297	0.135741	0.075965	3	3.290004
bZIP4	0.053225	0.063512	0.021171	3	3.137907
GATA20	0.049798	0.108344	0.112080	3	3.270221
NAC057	0.042927	0.566625	0.430884	3	4.040436
NAC2	0.034295	0.140722	0.336239	3	3.511256
AT5G41380	0.019630	0.333748	0.470735	3	3.824113
SVP	0.008801	0.146949	0.114570	3	3.270320
CRF1	0.004092	0.023661	0.094645	3	3.122398
SOL1	0.003865	0.089664	0.052304	3	3.145832
ROS4	0.001247	0.080946	0.051059	3	3.133252
SPL13A	0.001245	0.016189	0.014944	3	3.032379
AT5G16470	0.000699	0.105853	0.044832	3	3.151384
SPL12	0.000312	0.089664	0.047323	3	3.137298
NAC045	0.000051	0.046077	0.067248	3	3.113376
NF-YC3	0.000006	0.041096	0.075965	3	3.117067
AT3G20640	0.000003	0.062267	0.041096	3	3.103365
BRH1	0.000002	0.028643	0.069738	3	3.098383